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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
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Summarizing RNA-Seq Data or Differentially Expressed Genes Using Gene Set, Network, or Pathway Analysis.

Enrica Calura1, Paolo Martini2,3

  • 1Department of Biology, University of Padova, Padova, Italy.

Methods in Molecular Biology (Clifton, N.J.)
|April 9, 2021
PubMed
Summary
This summary is machine-generated.

Pathway and gene set analyses offer mechanistic insights into omics data. This study explores various analysis methods and visualization techniques using RNAseq and multiomics datasets for enhanced biological discovery.

Keywords:
Gene set analysisMultiomics data integrationNetworksPathwayTopological pathway analysis

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput studies generate vast amounts of omics data.
  • Pathway and gene set analysis provide mechanistic insights into this data.
  • These methods are crucial tools in modern omics research.

Purpose of the Study:

  • To provide mechanistic insights into high-throughput omics data.
  • To explore various gene set, network, and pathway analysis methods.
  • To highlight the importance of visualization in computational biology.

Main Methods:

  • Utilized two published RNA sequencing (RNAseq) datasets.
  • Applied freely available and updated bioinformatics software.
  • Performed gene set, network, and pathway analyses.
  • Conducted survival pathway analysis on a multiomics dataset.

Main Results:

  • Demonstrated the application of multiple analysis techniques on omics data.
  • Showcased the utility of visualization in interpreting complex biological data.
  • Provided a practical overview of available bioinformatics tools.

Conclusions:

  • Pathway and gene set analyses are essential for understanding omics data.
  • Visualization is a critical yet challenging aspect of computational analysis.
  • Freely available software enables comprehensive omics data interpretation.